‘We are drowning in data’: Retailers are getting smarter about data collections

Retailers have known for a long time that the collection and implementation of data is key to a modern, successful retail strategy — and now they’re getting smart about it.

For years, retailers were hungry for every source of data they could get their hands on, often stockpiling massive amounts of it. But now, they’re starting to understand that more data is not the goal to strive for. Instead, high-quality data — specifically, the kind that is actively given by consumers rather than collected passively — is the new goal.

“The question really is not between more data and less data, it’s between more data and better data,” said Josh Krepon, svp of global digital commerce at Cole Haan, at the National Retail Federation’s Big Show event in NYC on Tuesday.

There often a massive amount of data for brands to work with, but much of it is inaccurate or low-quality, or requires a lot of work to sort. Research from Digiday found that 82 percent of surveyed brands and retailers believe that third-party data is unreliable. Direct, self-reported data that customers actively volunteer is a much more valuable source.

“I mean we are drowning in data today,” said Jeff Neville, svp and practice lead at Boston Retail Partners. “I’ve spent hours getting sucked down the rabbit hole of Google Analytics, looking at various trends and things that don’t always lead anywhere. You need data that is accurate, that you can actually implement. There’s too much data around and not all of it is really useful.”

The best source of data for a lot of brands is the kind that is actively provided by the customer rather than the kind that is collected from them passively. Christiane Pendarvis, svp of e-commerce at FullBeauty Brands, a group of plus-size women’s fashion brands including FullBeauty, Jessica London and Ellos, pointed to user-generated content as being incredibly helpful, as it’s essentially self-reported data. Plus-size women’s brand Gwynnie Bee told Glossy earlier this week that they rely on direct feedback from customers to inform decision-making because that feedback is more reliable.

“It would make my job infinitely easier if I could always have clean, accurate, quality data,” Pendarvis said. “We have a lot of data about our customers, and it takes a lot of time just trying to make sure our information is right. Better data is way more valuable than more data.”

That kind of specific, reliable data can be a huge time and money saver for the brands. Pendarvis said FullBeauty pours considerable resources into designing, printing and mailing catalogs throughout the year, without accurate data tied to their effectiveness.

“Our largest women’s brand will sell 60 catalogs this year, more than one a week,” Pendarvis said. “It takes a tremendous amount of organizational work to do all those catalogs. We are trying to solicit feedback from customers to see which of them are actually transacting off of those catalogs and which aren’t. Who is the catalog gonna drive purchases for, and who will those catalogs drive high transaction value to? Machine learning can help us tie digital efforts back to the real world. If we can reduce our printing even by 10 percent, that’s millions of dollars back to us that we can reinvest elsewhere.”